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Author: Lenny

Long Beach Island has the most valuable hitting, and Crown Heights has the most valuable pitching (both starting rotation and bullpen).

The power ranking is based on player value for an average of all ABL parks. Batter value is the sum of the greatest combination of the eight fielding positions, plus the highest values of two remaining position players. Pitching values are from the top five starters and top four relievers (max one closer). Only the active roster is considered, apart from some assumptions about early-season starter taxi moves. The Titusville value is adjusted for system bias.

The scale is somewhat different from that of the pre-season ranking, which counted the maximum value of all players, without regard to position.

Power ranking is an estimate of team strength for entertainment purposes only and does not take into account management skill, trading savvy, or the luck of the dice.

Player values are determined as described here. Negative player values are ignored. Replacement levels are estimated for ABL 2019. Average batter & pitcher cards are estimated for ABL 2019. The keeps and draft picks are as of 2018-12-31, so no 2019 trades are included. The positions the keeps are rated for are not a factor, that is, if a team has three players who can play only third base, then all three of those players contribute to the total value.

This is a description of the player values that I compute for the ABL. I have bits and pieces of the explanation in various places, but thought it would be good to have everything in one place.

The basis of my calculations is linear weights, which is a method for estimating the number of runs produced by a player using the number of each play outcome for the batter. The particular variety of linear weights I use is called Extrapolated Runs. (See note below.) Each outcome is associated with a run value. A home run is 1.44 runs, a single is 0.5 runs, a strikeout is -0.098 runs. Note that the calculation can be done for both batters and pitchers. Of course, good batters will produce more runs, and good pitchers will allow fewer runs.

Now let’s consider a particular batter’s Triple Play Baseball card. If I can estimate the outcomes of each possible roll (000-999), then I can add up the run values (Extrapolated Runs) for each of those outcomes. If I divide that by 1000, then I have an average run estimate for one plate appearance by that batter. Note that I can do the same thing for a particular pitcher’s card.

To get all those outcomes requires a lot of data and a lot of estimates. The data part involves all the numbers in the main area of the card: this much of a home-run range, this much of an easy-fly range, etc. Then we need to create an average pitcher to face each batter, and vice versa. Then we need to estimate the number of times a batter will face righty and lefty arms, then weight those two values appropriately. We need to calculate the average outcomes of range plays and Deeps! But in the end we can get an estimated runs per plate appearance for every player.

Run values do not take into account the following ratings: injury, jump, steal, speed, hold, catcher throw, outfield throw, and double-play turn.

What’s missing at this point is defense. The Range and Error charts can be used to determine the runs saved by a defender using the same linear weights concept. These adjustments can be applied to a particular player, but if that player is carded at multiple positions, then the combined offensive-defensive run estimate is different for each position.

The goal is to calculate a player “value” that is something like WAR (Wins Above Replacement). Replacement players at different positions have different run-producing capacities. That holds true for both MLB and the ABL. For the ABL I set replacement levels close to the estimated run levels of the best available free agents at each position during the regular season. That level of runs at each position becomes the zero point of my calculated player value. The zero-adjusted run values are then scaled such that only the best players have a player value above 100. Players can have negative player values when free agents with higher run estimates are available at a position.

Defensive ability and position value can lead to very different player values for the same player. For example, an average-hitting catcher may have a significant value behind the plate, but a very low value playing first base, especially if his defense at first is FR/8.

Values are adjusted according to the average number of appearances as a full-time pitcher or position player. For example, on average closers will face fewer batters than a starter, so a closer’s value is adjusted down relative to a starter.

When I total the value of all players on a team, I do not count players with negative player value, because such players are unlikely to get lots of playing time. If a player plays multiple positions, I use the position with the highest value.

Since all free agents are rated, I can use historical ABL draft data to estimate the player value for various points in the draft.

TL;DR: The numbers and ratings on the cards are used to estimate the frequency of outcomes (single, home run, walk, strikeout, etc.). The outcomes are converted into runs using linear weights. The run estimate is adjusted for defense, then adjusted to a scale with zero indicating that an equivalent free-agent player is available, and 100 indicating an arbitrary superstar level.

A note on Extrapolated Runs
Extrapolated Runs (XR) appealed to me, because it is an estimate of absolute runs, unlike Palmer’s Batting Runs, which is measured relative to an average player. XR also includes double plays, which can be estimated from TPB cards.

The big weakness of XR is that it’s formulated to apply over a large span of seasons, specifically 1955-1997. I don’t find any XR coefficients for single, recent seasons.

Thought about this ordered dice system for some reason. The idea is to use multiple, uniformly colored, six-sided dice to produce a number of values, not all of which have the same probability. For example, with three dice, one can order the die values in ascending order, like 123, 256, 255, 224, 333, etc. This yields 56 possible values with the following probabilities.

I had a random thought about the differences between minor leagues in terms of being hitter-friendly or pitcher-friendly. I’ve often read qualifications of individual performances, for example, “he’s hitting well, especially since that’s a pitchers’ league,” or “his ERA is not bad, considering that he’s in a hitter-friendly league.” So I decided to go to the stats. I chose to compute the averages of the last five complete regular seasons, 2013-2017. But which stats to use? Runs per game? ERA? Batting average? I decided to compile OPS and ERA as the measurements for hitting and pitching, respectively. I knew that the two would be highly correlated, and that was indeed the case. I really didn’t see anything interesting by considering both stats together, so I simply sorted the leagues by OPS. The data appears in the table below.

I was surprised to see the huge difference between the top and the bottom: 126 points of OPS, 1.59 earned runs! The next surprise was that the leagues don’t cluster much by level. The Rookie leagues are all over the map.

I had a few ideas to explain the differences, then the Commish suggested a few others. Here’s a list of possible explanations.

Elevation. The Pioneer and Pacific Coast Leagues parks are generally at higher elevations, which helps the hitters.

Big Spring Training Parks. The Florida State League teams play in the Spring Training parks, which are big. The same probably goes for the Gulf Coast League, even though those are back fields.

Wood Bats. Hitters in the Short-Season A leagues may be at a disadvantage, because some of the hitters are using wood bats regularly for the first time.

Windy Florida. Maybe windy conditions are tough on the hitters in the Florida State League and GCL.

This analysis didn’t turn up much interesting. Although I’m not a fan of the DH rule, I had some ideas that the use of the DH had probably changed from its MLB inception in 1973 to the present day. I figured that the early DHs were the ageing sluggers like Cepeda & Oliva, and that the modern game uses a more mix-and-match approach to the DH. Nope.

I looked at regular-season starting lineups from the Retrosheet Event Files. I limited the analysis to American League lineups, because I wanted to focus on teams that used the DH most/all of the time. I included AL lineups in inter-league games when the DH was used.

I looked at the lineup slot occupied by the DH to see how that changed over the years. The table below shows the slots used for each season, 1973 through 2017. Cells are colored like a heat map, with red for the maximum and blue for the minimum.

I’m surprised how variable the data is from season to season. For example, in 1992 the DH led off 209 times (9.2%), and the following season the number was down to 32 (1.4%). Undoubtedly there were a couple of DHs in ’92 that led off regularly and did not do so in ’93. Still, the variation at all batting-order slots is more variable that I had expected. Maybe there’s a bit more consistency in the last ten years or so, but I didn’t do a numerical analysis of this.

Note that the only starting-lineup slot that was not filled by a DH for the entire season was the 9 spot, which had no DH in 1975 and 1997.

Of course, it’s clear that the DH is usually slotted in the heart of the lineup, and that hasn’t changed through history. The totals for all seasons are shown in the chart below. It’s no surprise to me that cleanup is the most common DH slot.

The other thing I looked at is how often a team used a single player as DH through the season. I looked at the number of games started by the most used DH on a team. The team with the most starts by one DH is plotted for each season, as is the team with the least starts by one DH. The mean plotted is the average of the DH leader of all teams. For example, in 1973 Orlando Cepeda started 142 games at DH for Boston (the max), while Kansas City had seven players with ten or more starts at DH, of whom Hal McRae had the most (33, the min).

The 1981 and 1994 seasons were shortened by strikes, so keep that in mind when looking at the data for those seasons.

There’s not much variation over history. I expected to see a decline in the max, but I don’t see it.

The coolest tidbit from this otherwise dull analysis was noticing that the maxima during 1978 & 1979 were 162, meaning that at least two players started every regular-season game at DH. That turned out to be Rusty Staub for the 1978 Tigers, and Willie Horton for the 1979 Mariners. Because of inter-league play, this record will likely never be broken!

The power rankings are based on the run value of players relative to a replacement player. Replacement-player values are based on post-draft free agents at each position, but these pre-season rating are based on replacement levels from the 2017 ABL season. The scale is set to zero for replacement players and 100 for an arbitrary “superstar” level. Run values are adjusted to expected game participation of full-time regular position players, starters, and relievers. Run values do not take into account the following ratings: injury, jump, steal, speed, hold, catcher throw, outfield throw, and double-play turn. Run values are based on an average of all current ABL parks.

The value of keeps are summed without regard to position. For example, if four keeps for one team can play only first base, all four are still counted.

The estimate value for draft picks is calculated differently from last season. Last season I used an average value from each round, taking into account the last few drafts. This time I assumed that the picks would proceed from the highest-value free agent and always proceed to the next highest-value free agent. This is not ideal, as value will tend to fall from the highest picks to lower picks, but at least it accounts for the order of picks within each round.

The family have a couple of tabletop games from the 70s that use funky, six-sided, wooden dice. (Superstar Baseball has a selection of all-time MLB greats, while Bowl Bound has college football teams from the 60’s & 70’s.) There are three dice: one black and two white. There are no pips on the dice—instead numerals are printed on the sides. It’s a bit like Strat-o-Matic. The black-die value is multiplied by ten, and the two white die are added to the total. So, for example, a black 2 and white 3 & 4 represent a value of 27. The faces of the dice are marked as follows:

The rating system is based on the run value of a player at a particular position, relative to a replacement player. Replacement-player values are based on post-draft free agents at each position. The scale is set to zero for replacement players and 100 for an arbitrary “superstar” level. Run values are adjusted to expected game participation of regular position players, starters, and relievers. Run values do not take into account the following ratings: injury, jump, steal, speed, hold, catcher throw, outfield throw, and double-play turn. Run values are based on an average of all current ABL parks.

Team power rankings are calculated by adding the run values of 19 players on each team:

8 position players chosen for maximum value as a group (Platoons are not considered.)

2 additional position players, which represent DH and bench strength

5 most valuable starters

4 most valuable relievers

The post-draft power rankings are depicted in the chart below.

Syracuse has the strongest position players, Orlando has the weakest. La Jolla has the strongest starting rotation, Ocracoke has the weakest. Chesapeake Bay has the strongest bullpen, Mudville has the weakest.

The power rankings are a simple measurement of team strength and may not accurately predict win/loss records.

The most significant feature of this year’s draft was the lack of starting pitching. Relief pitching was good at the top, but poor in the middle. Batters in the draft pool were stronger than normal. Titusville’s picks went very much according to plan for the first five rounds or so, before the normal confusion set in.

The draft pool was quite weak this year, despite the fact that two extra pool teams were left in upon the Gangsta’s last-minute withdrawal. Titusville lacked a second-round pick this year, having traded it away for Yasmani Grandal.

Recently a Binghamton Mets fan commented that AA is a high level of ball for a community the size of Binghamton. Is that true? Let’s take a look at the populations of the metro areas in the Eastern League. The whole metro-area thing is not an exact science, but I think most of the EL cities are reasonably represented. I used a list on Wikipedia that has 2012 population figures. New Hampshire is represented by Manchester, and New Britain is represented by Hartford. The only real choice for Bowie is Washington. Bowie is a bit of an anomaly in the Eastern League, as it’s the only location that very close to an MLB city. The metro-area populations are shown in the chart below. Bowie is not included, because Washington’s nine million population is off the charts.

I’m surprised that New Britain/Hartford is the largest (apart from Bowie/DC). Anyway, the fan was correct: only Altoona has a lower population than Binghamton. I’ve been to Altoona, and not only is it fairly small, but it’s also pretty isolated. I can’t imagine that many people make the trek from Pittsburgh or State College. It’s a fairly new site for organized baseball (1999), and their attendance is relatively strong. Well done, Altoona!

Speaking of attendance, the average EL 2014 home attendance is shown in the chart below.

Binghamton’s place in the cellar may play a large role in the possible demise of the franchise, but it’s been fun while it’s lasted!

I expect to go from first to worst this season, as there wasn’t a good return from last year’s championship squad. In fact, I kept only 13, so had two extra picks, including the last of the entire draft. The strategy was to invest in young position players that might take major strides in 2015. Pitching was relegated to the later rounds. I was hoping to snag Starlin Castro and Mookie Betts, but LBI was clever enough to pounce before me.

During the 2014 ABL season everyone noticed the increase in pitcher cards with the R symbol. I wrote about it inthe 2014 ABL Yearbook. Now that my 2015 card data is in the computer, it’s a good opportunity to see if the Rs are still as numerous. I did a simple count of the pitchers in recent seasons that have each symbol. Starters and relievers are all grouped together. The data is from only the pitchers with ABL eligibility; not all Triple Play cards are represented. I don’t think I’ve missed too many eligible players over the last few seasons, but the first couple of seasons considered here are probably missing a few, especially for the 2008 season. The years listed refer to the ABL season, so the 2015 data is from the 2014 Triple Play cards that we’ll be using in the upcoming 2015 ABL season. OK, enough of the fine-print bullshit, let’s go to the graphs.

Well, it looks like 2014 was a blip for the R symbol. The frequency has dropped down to the previous level.

The H symbol continues to occur infrequently. (To the relief of all ABL managers!) It’s interesting that the level of the H symbol seems to follow that of the R from year to year. I didn’t notice that before, probably because the yearbook study weighted the symbols by how many innings were pitched in the ABL, and nobody likes to give an H pitcher a lot of innings. In 2014, when the R frequency doubled, the H frequency doubled too, from 4.5% to 9.5%! In 2015 it’s back down to 4.5%.

The L symbol is back with a vengeance! Lots of shorts have the L this year, and it looks like every single qualified closer has one. In the yearbook I speculated that the combination of B & L might be constant. It sure doesn’t look like that in 2015. This season should see more walks than ever before erased from batter cards, because the frequency of Bs is up too.

And finally, the F symbol (found on relievers’ cards only) has not fluctuated much over the years.

In summary, compared to last season, expect fewer homers & deeps to be re-rolled, and expect to lose more walks off the batters card.

Commish & I were discussing the standards for official scorers giving errors. Should the same standard be applied regardless of the level, or should the standards be higher at the higher levels?

Commish made the excellent point that throwing errors (especially to first) are going to be automatic and are not really subject to any subjective standard. Since these types of errors are obviously made more frequently at the lower levels, we expect the number of errors to go up as the level goes down.

So, I can’t answer my original question with stats, but I still thought it would be interesting to look at the fielding percentages at the different levels of OB. I used 2013 stats and excluded leagues south of the border.

The trend is clear. Actually, it’s clearer than I expected! When you get down to A ball, errors are twice as likely compared to the Bigs.